Rank: Driverless AI
Description: Learn about the complext feature engineering that Driverless AI does. How to squeeze more performance out of your machine leanring models.
Dmitry Larko, Kaggle Grandmaster, and Senior Data Scientist at H2O.ai goes into depth on how to apply feature engineering in general and in Driverless AI. This video is over a year old and the version of Driverless AI shown is in beta form. The current version is much more developed today.
This is by far one of the best videos I’ve seen on the topic of feature engineering, not because I work for H2O.ai, but because it approaches the concepts in an easy to understand manner. Plus Dmitry does an awesome job of helping watchers understand with great examples.
The question and answer part is also very good, especially the discussion on overfitting. My notes from the video are below.
Feature engineering is extremely important in model building
“Coming up with features is difficult, time-consuming, requires expert knowledge. “Applied machine learning” is basically feature engineering” - Andrew Ng
Common Machine Learning workflow (see image below)
null- What is feature engineering? Example uses Polar coordinate conversions for linear classifications
Creating a target variable is NOT feature engineering
Removing duplicates/Missing values/Scaling/Normalization/Feature Selection IS NOT feature engineering
Feature Selection should be done AFTER feature engineering